---
id: doc-summarization-using-datasets
title: Pulling your Dataset for Evaluation
sidebar_label: Pulling Dataset for Evaluation
---

To **start using your legal document dataset for evaluation**, you’ll need to:

1. Pull your dataset from Confident AI.
2. Compute the summaries.
3. Begin running evaluations.

## Pulling Your Dataset

Pulling a dataset from Confident AI is as simple as calling the `pull` method from an `EvaluationDataset` and providing the dataset alias, or name that you defined on Confident AI.

```python
from deepeval import EvaluationDataset

dataset = EvaluationDataset()
dataset.pull(alias="Legal Documents Dataset", auto_convert_goldens_to_test_cases=False)
```

:::note
By default, `auto_convert_goldens_to_test_cases` is `True`, but it will raise an error if your dataset, `Legal Documents Dataset`, hasn't been populated with summaries in the `actual_output` field, which is a mandatory field in a test case. [Learn more about test cases here](/docs/evaluation-test-cases).
:::

## Converting Goldens to Test Cases

Next, we'll convert the goldens in the dataset we pulled into `LLMTestCase`s and add them to our evaluation dataset. This is much simpler than parsing your PDF documents every single time you run an evaluation!

```python
from deepeval.test_case import LLMTestCase

for golden in dataset.goldens:
    actual_output = llm.summarize(golden.input)  # Replace with logic to compute actual output

    dataset.add_test_case(
        LLMTestCase(
            input=golden.input,
            actual_output=actual_output,
        )
    )
```

## Evaluating Your Dataset

Finally, run the `evaluate` function to run evaluations on your newly pulled dataset.

```python
from deepeval import evaluate

...
evaluate(
  dataset,
  metrics = [concision metric, completeness_metric], # add more metrics as you deem fit
  hyperparameters={"model": model, "prompt template": prompt_template}
)
```
